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Your Shopping Cart just got a whole lot Smarter, this festive season

7 minutes, 7 seconds read

The shopping season has officially returned to the Indian subcontinent. While the first phase of festivities (typically) kicks off with the onset of Navratri (sep 29) till Dussehra (oct 8), Indian retailers will have clocked above 40% of their annual sales within this ten day window alone. For consumers, ‘better deals’ take precedence over attributes like faster shipping during this season. In fact, retailers will have adjusted their pricing to strongly reflect these consumer preferences — a pair of women’s running shoes, for instance, will have a discounted price of 19% pre-diwali and upto a flat 50% discounted price on the day of. 

In a country with over 400M active online users, customer fealty during this season is even more fickle than usual. The growing number of online consumers are heralding new buying behaviors especially from tier 2 and 3 cities. According to Google Insights, 70% of Indian netizens go online during the festive season to browse products, compare prices, read reviews and look for deals. For brands & retailers, getting in front of these potential customers and clamoring for their attention is the pivotal moment of truth. 

Amazon and Walmart-owned Flipkart, India’s top two e-tailers, are using intelligent technologies to stave off each other’s aggressive discounting strategies. The two e-commerce giants have cumulatively created over 140,000 temporary jobs across supply chain, last-mile connectivity and customer support to handle the extra influx of trade. Daily shipments in India is expected to touch 4 million units during the ongoing festive season.

AI in e-commerce:India's e-retail market share of gross merchandise value.

Which begs the question: How are they doing this? How are they using technology to stay-on-top?

It’s no secret, the retail spend on AI is forecast to grow from $2 billion in 2018 to $7.3 billion by 2022, according to Juniper Research.
In reality, they rely on Artificial Intelligence — it is where these companies have primarily invested a huge chunk of change to enhance their business. By leveraging the right set of AI-assisted tools in their operations, they are able to retain and convert more customers. 

Artificial Intelligence and related technologies like machine learning and natural language processing has intensified over the digital buying landscape. This has forced brick & mortar stores including physical outlets with omni channel reach to a receding corner of the industry.

There’s more to the digital landscape than meets the eye. It is a space plagued with security concerns. E-commerce companies are using AI to detect and eliminate potential frauds on their platform. They’ve deployed AI models that constantly vets fraudulent accounts that have only signed up to make the most of promo codes, or bring cash out of stolen credit cards. 

Yes, aggressive pricing does work as reflected by the higher EMI adoption this year. However, cash burn through discounts is not the overhaul the industry can sustain itself on. Big Data Analytics can prescribe a more proactive approach for suggestions based on statistical association evaluation, time spent on site, cookies behavior and method of accessing site which can tell a brand the how, what and when of the customer buying cycle — in turn, increasing sales.

AI has even infiltrated physical retail, and is now helping stores maximise marketing efforts, personalise the customer experience and optimise their store inventory.

AI in retail market

Warehouses and stores, in India, are also making use of ‘Cobots (collaborative robots) to assist humans in performing tedious and repetitive shop-floor tasks. The cobots run on machine learning algorithms that have defined its capacity to perform specific tasks while also learning to get better with new data.

Ahead of this year’s festive sale, Flipkart has added 340 cobots or automated guided vehicles (AGVs) to its current fleet of 110. These bots can carry anything with them, from appliances to mobile phones. 

After the first phase of the festive shopping marathon, Amazon and Flipkart have both made significant wins over the period. They will look to extend their market capture as we move into the second phase of the season (Diwali).

Interestingly, for Amazon, almost half the product sales came from lower-tier urban areas. Amazon India-owned Echo products even saw a record 70 fold increase in sales.

Flipkart receives over 90% of traffic from its android app, and has designed its app home screen personalized to each of its 120 million+ customers. They have deployed machine learning models and algorithms on various customer data points like customer location, language, gender, price, affinity to a store or brand, purchasing frequency, purchase volume, price group, etc. among others.

These data points help Flipkart make predictions even without the customer being on their platform. Using these machine learning models they are also able to predict if a customer is going to return a particular product.

This season, customers can continue to expect strides in personalization and tailored experiences. E-tailers can expect to see improvements to their order handling, and personalization efforts. Overtime, these improvements will pay dividends in the form of revenue enhancement, increased margins, and higher sales.

How can AI upscale e-commerce

AI has made smooth inroads into digital shopping aisles — with several intelligent use cases such as stock assortment, fraud reduction and self-checkout. Here is a brief compilation of adopted strategies used in retail with the potential to disrupt the future of online shopping.

Product Recommendations

Recommendation engines have become a staple of commercial AI usage. By looking at customers’ purchase histories, current activity (cart contents and page views), and other linked third-party data, e-tailers can make highly tailored suggestions. Amazon, for example, makes more than 40% of its sales via their recommendation engine which also suggests items based on what your friends have purchased recently.Demand Forecasting
E-tailers expect to know in advance how much of each product is projected to especially during peak season. AI can enhance demand predictions by minimizing overstock and out-of-stock situations. ML Algorithms can optimise what products should be made available in a particular geography. For example, Levi’s is using AI to improve size availability, and Nike is using geographical and behavioural data from its app to inform store offerings.

Personalization

AI systems can capture deep customer insights about their buying preferences and behavior using their social data, purchase history, and browsing habits. AI can fill in the gaps by looking at a user’s spending patterns and other data sources to come up with a very detailed view of the customer. This has proven to enhance the customer’s digital shopping experience with a more satisfying view of highly relevant and hyper-personalized offerings.

Shopping Assistant

An AI-powered shopping assistant is a natural extension of the chatbot, with layers of visual processing added in. For example, if a customer wants to choose an outfit for a special occasion. The AI shopping assistant could learn their tastes and help them select some garments. It could then walk them through the process of virtually trying on an outfit (virtual trial rooms). It could offer suggestions for complementary items or encourage them to buy the product, as a friend might. The shopping assistant can also suggest the complementary outfits, footwear and accessories just like a real fashion assistant/advisor would.

Swift Customer Service

Primarily dominated by chatbots over the last several years, bots can learn from the interactions between customers and human reps. Chatbots are trained using natural language processing techniques to understand jargon and ‘speech’ specific to retail. They can then use the data it harvests to create a more personable interaction. It can also quickly reduce the number of touchpoints for the customer and help address immediate queries related to pricing, product availability, returns and recommendations without the need for human intervention.

Also read – How Chatbots are changing the digital Indian?

Smarter Voice Searches

Voice-powered searches can act on a ton of customer insights and information fed into the recommendation engine from the customer’s profile. Voice-activated shopping, is a natural extension of human behavior — allowing consumers to take control of the omnichannel experience to learn more about the product, gather quick product information, compare prices etc. Orders placed via Alexa have increased three times more than the year-ago festive shopping season.

Product identification & visual search

esearch has shown customers who gravitate towards voice-powered searches, equally embrace visual searches. For example, an AI-powered matching algorithm could look at the images of a customer’s favorite products (shirts, sneakers etc.) and suggest similar ones based on attributes like pattern, fit, color, style etc. The AI program can also identify products kept in cart and website pages from browsing based on the customers’ past shopping data and other data from various sources, making the suggestions more accurate with time.

To know more about how Artificial Intelligence can help increase your persona capture and retention, reach out to us on hello@mantralabsglobal.com.

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Retention playbook for Insurance firms in the backdrop of financial crises

4 minutes read

Belonging to one of the oldest industries in the world, Insurance companies have weathered multiple calamities over the years and have proven themselves to be resilient entities that can truly stand the test of time. Today, however, the industry faces some of its toughest trials yet. Technology has fundamentally changed what it means to be an insurer and the cumulative effects of the pandemic coupled with a weak global economic output have impacted the industry in ways both good and bad.

Chart, line chart

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Source: Deloitte Services LP Economic Analysis

For instance, the U.S market recorded a sharp dip in GDP in the wake of the pandemic and it was expected that the economy would bounce back bringing with it a resurgent demand for all products (including insurance) across the board. It must be noted that the outlook toward insurance products changed as a result of the pandemic. Life insurance products were no longer an afterthought, although profitability in this segment declined over the years. Property-and-Casualty (P&C) insurance, especially motor insurance, continued to be a strong driver, while health insurance proved to be the fastest-growing segment with robust demand from different geographies

Simultaneously, the insurance industry finds itself on the cusp of an industry-wide shift as technology is starting to play a greater role in core operations. In particular, technologies such as AI, AR, and VR are being deployed extensively to retain customers amidst this technological and economic upheaval.

Double down on digital

For insurance firms, IT budgets were almost exclusively dedicated to maintaining legacy systems, but with the rise of InsurTech, it is imperative that firms start dedicating more of their budgets towards developing advanced capabilities such as predictive analytics, AI-driven offerings, etc. Insurance has long been an industry that makes extensive use of complex statistical and mathematical models to guide pricing and product development strategies. By incorporating the latest technological advances with the rich data they have accumulated over the years, insurance firms are poised to emerge stronger and more competitive than ever.

Using AI to curate a bespoke customer experience

Insurance has always been a low-margin affair and success in the business is primarily a function of selling the right products to the right people and reducing churn as much as possible. This is particularly important as customer retention is normally conceived as an afterthought in most industries, as evidenced in the following chart.

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        Source: econconusltancy.com

AI-powered tools (even with narrow capabilities) can do wonders for the insurance industry at large. When architected in the right manner, they can be used to automate a bulk of the standardized and automated processes that insurance companies have. AI can be used to automate and accelerate claims, assess homeowner policies via drones, and facilitate richer customer experiences through sophisticated chatbots. Such advances have a domino effect of increasing CSAT scores, boosting retention rates, reducing CACs, and ultimately improving profitability by as much as 95%.

Crafting immersive products through AR/VR

Customer retention is largely a function of how good a product is, and how effective it is in solving the customers’ pain points. In the face of increasing commodification, insurance companies that go the extra mile to make the buying process more immersive and engaging can gain a definite edge over competitors.

Globally, companies are flocking to implement AR/VR into their customer engagement strategies as it allows them to better several aspects of the customer journey in one fell swoop. Relationship building, product visualization, and highly personalized products are some of the benefits that AR/VR confers to its wielders.  

By honoring the customer sentiments of today and applying a slick AR/VR-powered veneer over its existing product layer, insurance companies can cater to a younger audience (Gen Z) by educating them about insurance products and tailoring digital delivery experiences. This could pay off in the long run by building a large customer base that could be retained and served for a much longer period.

The way forward

The Insurance industry is undergoing a shift of tectonic proportions as an older generation makes way for a new and younger one that has little to no perceptions about the industry. By investing in next-generation technologies such as AR/VR, firms can build new products to capture this new market and catapult themselves to leadership positions simply by way of keeping up with the times.

We have already seen how AR is a potential game-changer for the insurance industry. It is only a matter of time before it becomes commonplace.

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